Defect segmentation on 'Jonagold' apples using colour vision and a Bayesian classification method

This paper shows how the information enclosed in a colour image of a bi-colour apple can be used to segment defects. A method to segment pixels, based on a Bayesian classification process, is proposed. The colour frequency distributions of the healthy tissue and of the defects were used to estimate the probability distribution of each class. The results showed that most defects, namely bitter pit, fungi attack, scar tissue, frost damages, bruises, insect attack and scab, are segmented. However, russet was sometimes confused with the transition area between ground and blush colour. (C) 1999 Elsevier Science B.V. All rights reserved